中国股市财富效应分析

由于股票价格上涨导致股票持有人财富增加,进而产生消费增加的效应就是股票市场的财富效应。美国股市和中国股市运行过程中的经验证明,财富效应是一个客观存在。由于我国股市规模较小,流通市值较少,投资者结构和收益分配结构不合理.股市波动频率过快、波幅过大以及股市噪声等诸多不利因素的影响,导致财富效应还不显著。     

抓紧设立股市平准基金

王连洲:建立平准基金是需要的很多国家都采取了一切必要的措施来应对此次金融动荡,中国难以独善其身,也在采取力度较大的财政和货币政策。在这种情况下,建立股市平准基金以平抑股市的动荡波幅,保证股市的稳健运行,我觉得是需要的。平准基金根据市场的表现适时吞吐,可以起到调节和平衡市场的作用。     

驳"股市泡沫论"

中国股市近几月上涨很快,但是否完全是泡沫经济呢?中国股市上涨原因如下:(1)政府的呵护支持;从2004年4月"国九条"起,中共中央、国务院一直希望股市走出熊市的阴影.     

警惕股市泡沫

香港和上海股市可能已经出现了泡沫的苗头.内地股市出现泡沫,其原因在于由中国银行系统低存贷比所反映出来的流动性过剩.过度的流动性已经使股市翻番;而在未来,中国股市可能会经历一次痛苦的调整,如同2001年后所发生的那样.     

论股市信心

股市信心是股市健康发展的决定因素。股市信心的缺失造成了我国股市的一度低谜,要使股市保持健康稳定的发展,必须重塑股市信息。     

论和谐股市与经济发展

和谐股市的内涵与主要内容 和谐股市是一个系统概念,但就其主要内容而言,应该包括以下几方面:     

股市水流论

股市水流论：股价的运行类似于水流,股市投资策略要充分利用这一规律,才能获取最大的收益。了解股市水流论,对于投资者顺势而为和找到价格运行的临界点,有着重大的现实意义。股市水流论适用于证券投资的所有领域。     

中国央行降准的实质是护航股市

4月17日，在中国内地及香港地区股市收市之后，中国证监会发出了管理好证券市场融资融券风险的七条通知，并希望以市场化的方式（如鼓励沽空或融券）来达到相应的目的。而该消息一公布，立即触发了香港晚上的股市期货指数大跌，整个国外市场反应十分激烈。之后星期六（4月18日）中国证监会立即出来表示，上周五公布的七项管理融资融券风险的政策只是原有政策，也不是鼓励股市沽空，更不是要打压股市，请求市场正确理解，希望以此来稳定股市之信心。     

功能错位的中国股市

在寻求国有企业风险转移途径的过程中，政府借助国有股东身份谋利，从而使股市的微观功能被过分放大、宏观功能被扭曲，进而造成各种市场问题，导致金融系统风险不断累积。因此，政府必须尽快退出国有股东特权地位，停止向股市转移风险，以充分发挥股市的资源优化配置功能。     

上海和香港两地股市联动性研究——基于GARCH模型的分析

本文基于经济基础假说和市场传染假说两大基础理论,将股票收益率分解为开盘收益率和收盘收益率,运用GARCH-M模型研究了上海股市和香港股市之间的联动关系。结果显示,两大股市存在相互影响的联动关系,但是上海对香港股市的影响要强于香港对上海股市的影响,反映出两地之间的紧密经济关系及大陆对香港地区经济影响日益增强的现实。     

Does an intertemporal tradeoff between risk and return explain mean reversion in stock prices?

When volatility feedback is taken into account, there is strong evidence of a positive tradeoff between stock market volatility and expected returns on a market portfolio. In this paper, we ask whether this intertemporal tradeoff between risk and return is responsible for the reported evidence of mean reversion in stock prices. There are two relevant findings. First, price movements not related to the effects of Markov-switching market volatility are largely unpredictable over long horizons. Second, time-varying parameter estimates of the long-horizon predictability of stock returns reject any systematic mean reversion in favour of behaviour implicit in the historical timing of the tradeoff between risk and return.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Firm level return-volatility analysis using dynamic panels

This paper examines “leverage” and volatility feedback effects at the firm level by considering both market and firm level effects, using 242 individual firm stock data in the US market. We adopt a panel vector autoregressive framework which allows us to control simultaneously for common business cycle effects, unobserved cross correlation effects in return and volatility via industry effects, and heterogeneity across firms. Our results suggest that volatility feedback effects at the firm level are present due to both market and firm effects, though the market volatility feedback effect is stronger than the corresponding firm level effect. We also find that the leverage effect at the firm level is persistent, significant and negative, while the effect of market return on firm volatility is persistent, significant and positive. The presence of these effects is further explored through the responses of the model's variables to market-wide return and volatility shocks.     

Causality in volatility and volatility spillover effects between US, Japan and four equity markets in the South China Growth Triangular

In examining co-movement across international stock markets, previous researchers usually pre-determine the direction of causation and neglect the Chinese equity markets. In this study, we examine the spillover effects of volatility among the two developed markets and four emerging markets in the South China Growth Triangular using Chueng and Ng's causality-in-variance test. Several findings deserve mention: (1) the Japanese stock market affects the US stock market and there is a feedback relationship between the Hong Kong and US stock market. (2) Markets of the SCGT are contemporaneously correlated with the return volatility of the US market. (3) Econometric models constructed according to the results of variance-in-causality tests have greater explanatory power than the conventional GARCH(1,1) model. (4) Using the return volatility of foreign exchange as a proxy for informational arrival can explain excess kurtosis of a stock return series, especially for the less open emerging market. (5) Geographic proximity and economic ties do not necessarily lead to a strong relationship in volatility across markets.     

Modelling return and conditional volatility exposures in global stock markets

This article empirically investigates the exposure of country-level conditional stock return volatilities to conditional global stock return volatility. It provides evidence that conditional stock market return volatilities have a contemporaneous association with global return volatilities. While all the countries included in the study exhibited a significant and positive relationship to global volatility, emerging market volatility exposures were considerably higher than developed market exposures. JEL Classification G12     

Dynamic analysis of stock return volatility in an integrated international capital market

This paper examines the dynamic behavior of the stock return volatility for Canada, Japan, Germany, and the United Kingdom. The evidence indicates that international stock return volatility is mainly influenced by the U.S. stock return volatility and the exchange rate volatility, supporting the international capital market integration hypothesis. There seems to be some correlation between stock return volatility and macroeconomic volatility, but the effect is relatively weaker. In addition to the economic fundamentals, the noise component is found to be time varying, confirming the AR(MA)CH specifications in the stock return models.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Asymmetric risk premium in value and growth stocks

There are two competing explanations for the existence of a value premium, a rational market risk explanation, whereby value stocks are inherently more risky than growth stocks, and a market over-reaction hypothesis, where agents overstate future returns on growth stock. Using asymmetric GARCH-M models this paper tests the predictions of the two hypotheses. Specifically, examining whether returns exhibit a positive (negative) risk premium resulting from a negative (positive) shock and the relative size of any premium. The results of the paper suggest that following a shock, volatility and expected future volatility are heightened, leading to a rise in required rates of return which depresses current prices. Further, these effects are heightened for value stock over growth stock and for negative shocks over positive shocks. Thus, in support of the rational risk interpretation, with a volatility feedback explanation for predictive volatility asymmetry.     

Speculative dynamics and price behavior in the Shanghai Stock Exchange

This article examines the extent to which the trading behavior of heterogeneous investors manifests in stock price changes of asset portfolios which constitute the Shanghai Stock Exchange. There are three major findings that materialize. Firstly, reliable statistical evidence of a negative relation between the conditional first and second moments of the return distributions of stock prices lends support to the volatility feedback effect. Secondly, ‘feedback’, or momentum-type investors, are not present in this market as is often detected from the daily price changes of other industrialized markets. Finally, trade volume as a proxy for ‘information-driven’ trading suggests that such investors play a statistically significant role in stock price movements. Parameter estimates from this latter group of investors imply that a rise in stock prices from a high volume trading day is more likely than a rise resulting from a low volume trading day.     

MEAN AND VOLATILITY SPILLOVERS ACROSS MAJOR NATIONAL STOCK MARKETS: FURTHER EMPIRICAL EVIDENCE

This paper provides additional insight into the nature and degree of interdependence of stock markets of the United States, Japan, the United Kingdom, Canada, and Germany, and it reports the extent to which volatility in these markets influences expected returns. The analysis uses the multivariate GARCH-M model. Although they are considered weak, statistically significant mean spillovers radiate from stock markets of the U.S. to the U.K., Canada, and Germany, and then from the stock markets of Japan to Germany. No relation is found between conditional market volatility and expected returns. Strong time-varying conditional volatility exists in the return series of all markets. The own-volatility spillovers in the U.K. and Canadian markets are insignificant, supporting the view that conditional volatility of returns in these markets is “imported” from abroad, specifically from the U.S. Significant volatility spillovers radiate from the U.S. stock market to all four stock markets, from the U.K. stock market to the Canadian stock market, and from the German stock market to the Japanese stock market. The results are robust and no changes occur in the correlation structure of returns over time.